Linkage Effects and Analysis of Finite Sample Errors in the HapMap

Zaitlen, Noah; Kang, Hyun Min; Eskin, Eleazar

doi:10.1159/000212500

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Thus, researchers who are interested in using a different statistic, testing a different hypothesis (e.g., that the disease follows a dominance model and not an additive one), or optimizing a different metric for power are recommended to perform a set of simulations based on the framework suggested here. Furthermore, error in the variance-covariance matrix as a result of a finite reference sample size, 20 as well as errors in the estimation of the relative risk, such as those from the winner's curse, might disturb the accuracy of the LSR estimates in MULTIPOP and, hence, the final design choices. The simulated data sets used in this work were based on the HapMap genotypes.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Genetic Variability across Populations for the Identification of Causal Variants

Zaitlen

Paşaniuc

Gur

et al. 2010

The American Journal of Human Genetics

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147

122

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Genome-wide association studies have been performed extensively in the last few years, resulting in many new discoveries of genomic regions that are associated with complex traits. It is often the case that a SNP found to be associated with the condition is not the causal SNP, but a proxy to it as a result of linkage disequilibrium. For the identification of the actual causal SNP, fine-mapping follow-up is performed, either with the use of dense genotyping or by sequencing of the region. In either case, if the causal SNP is in high linkage disequilibrium with other SNPs, the fine-mapping procedure will require a very large sample size for the identification of the causal SNP. Here, we show that by leveraging genetic variability across populations, we significantly increase the localization success rate (LSR) for a causal SNP in a follow-up study that involves multiple populations as compared to a study that involves only one population. Thus, the average power for detection of the causal variant will be higher in a joint analysis than that in studies in which only one population is analyzed at a time. On the basis of this observation, we developed a framework to efficiently search for a follow-up study design: our framework searches for the best combination of populations from a pool of available populations to maximize the LSR for detection of a causal variant. This framework and its accompanying software can be used to considerably enhance the power of fine-mapping studies.

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Section: Discussionmentioning

confidence: 99%

Leveraging Genetic Variability across Populations for the Identification of Causal Variants

Zaitlen

Paşaniuc

Gur

et al. 2010

The American Journal of Human Genetics

Self Cite

147

122

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show abstract

“…Differences between the study population and the HapMap, the genotyping density and the finite size of the HapMap can effect this estimate of correlation [Zaitlen et al, 2009]. We examine the relation between the true r i,j and this estimate of imputation quality over several data sets.…”

Section: Methodsmentioning

confidence: 99%

Imputation aware meta‐analysis of genome‐wide association studies

Zaitlen

Eskin

2010

Genetic Epidemiology

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Genome-wide association studies have recently identified many new loci associated with human complex diseases. These newly discovered variants typically have weak effects requiring studies with large numbers of individuals to achieve the statistical power necessary to identify them. Likely, there exist even more associated variants, which remain to be found if even larger association studies can be assembled. Meta-analysis provides a straightforward means of increasing study sample sizes without collecting new samples by combining existing data sets. One obstacle to combining studies is that they are often performed on platforms with different marker sets. Current studies overcome this issue by imputing genotypes missing from each of the studies and then performing standard meta-analysis techniques. We show that this approach may result in a loss of power since errors in imputation are not accounted for. We present a new method for performing meta-analysis over imputed single nucleotide polymorphisms, show that it is optimal with respect to power, and discuss practical implementation issues. Through simulation experiments, we show that our imputation aware meta-analysis approach outperforms or matches standard meta-analysis approaches.

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“…Summary statistics-based methods requiring an estimate of the genetic correlation matrix are becoming increasingly popular; however, very few GWAS include LD information in their released data. In prior work, this information has been approximated by using LD information from ‘best guess’ reference panels, but here we show that this can lead to high error rates even when a population closely matching the study population is available ( Zaitlen et al ., 2009 ). Our method can be used to improve the accuracy of any summary statistics-based method that requires LD information by more accurately estimating the local genetic correlation structure using information available across several reference populations.…”

Section: Discussionmentioning

confidence: 94%

Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses

et al. 2015

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Motivation: Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global ‘best guess’ reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations.Results: We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data. We evaluated our method by measuring the performance of two summary statistics-based methods: imputation and joint-testing. When using our method as opposed to the current standard of ‘best guess’ reference panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease in mean-squared error for joint-testing.Availability and implementation: Our method is publicly available in a software package called ADAPT-Mix available at https://github.com/dpark27/adapt_mix.Contact: noah.zaitlen@ucsf.edu

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Linkage Effects and Analysis of Finite Sample Errors in the HapMap

Cited by 5 publications

References 32 publications

Leveraging Genetic Variability across Populations for the Identification of Causal Variants

Leveraging Genetic Variability across Populations for the Identification of Causal Variants

Imputation aware meta‐analysis of genome‐wide association studies

Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses

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